Pre-training, Reasoning, Benchmarking: X-ray Report Generation on CheXpert Plus Dataset
Quick Answer
The study introduces MambaXray-PRB, a novel framework for X-ray report generation leveraging the CheXpert Plus dataset.
Quick Take
It addresses the lack of standardized benchmarks and enhances report generation performance through multi-stage pre-training and multi-modal reasoning, validated across multiple datasets.
Key Points
- MambaXray-PRB improves X-ray report generation performance significantly.
- The framework uses multi-stage large-model pre-training and reasoning.
- A comprehensive benchmark for X-ray report generation models is established.
- Extensive experiments validate MambaXray-PRB on IU X-ray, MIMIC-CXR, and CheXpert Plus.
- The CheXpert Plus dataset lacks baseline implementations, hindering evaluation.
DeepSignal Analysis
What happened
The study presents MambaXray-PRB, a framework for X-ray report generation using the CheXpert Plus dataset. It aims to address the lack of standardized benchmarks in the field and improve report generation through multi-stage pre-training and multi-modal reasoning.
Key evidence
- The CheXpert Plus dataset lacks baseline implementations and evaluation results, which limits standardized training and fair comparisons among algorithms.
- MambaXray-PRB employs a three-phase pipeline: self-supervised auto-regressive modeling, X-ray-report contrastive learning, and post-training optimization.
- Experiments conducted on IU X-ray, MIMIC-CXR, and CheXpert Plus datasets demonstrate the effectiveness of MambaXray-PRB for radiology report generation.
Why it matters
The introduction of MambaXray-PRB could significantly enhance the efficiency of X-ray report generation, which is crucial for reducing clinician workload and improving patient care. By establishing a benchmark, the study facilitates better evaluation and comparison of future models, potentially accelerating advancements in medical AI.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract:X-ray image-based Radiology Report Generation (RRG) constitutes a critical research direction within medical artificial intelligence, with great potential to alleviate clinicians' diagnostic workload and shorten patient waiting periods. Despite substantial advances over recent years, the field faces evident bottlenecks stemming from insufficient standardized benchmarks and inadequate domain adaptation of generic large models. Notably, the newly released CheXpert Plus dataset is provided without accompanying baseline implementations and evaluation results, which impedes standardized training, quantitative evaluation and fair comparison among follow-up algorithms. To mitigate this limitation, we establish a comprehensive benchmark encompassing prevailing X-ray report generation models and Large Language Models on CheXpert Plus. This benchmark delivers a reliable comparative foundation for upcoming methods and enables researchers to rapidly identify state-of-the-art approaches within this domain. Beyond benchmark construction, we rethink X-ray RRG under the paradigm of large models and propose a novel framework termed MambaXray-PRB. Our framework improves report generation performance and enhances model interpretability via multi-stage large-model pre-training and multi-modal Chain-of-Thought reasoning. The pipeline consists of three successive phases: self-supervised auto-regressive modeling, X-ray-report contrastive learning, and post-training optimization for reasoning and report generation. Extensive experiments on IU X-ray, MIMIC-CXR, and CheXpert Plus datasets validate the effectiveness of MambaXray-PRB for radiology report generation. The source code of this paper is available on this https URL
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.08813 [cs.CV] |
| (or arXiv:2610.08813v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08813 arXiv-issued DOI via DataCite |
Submission history
From: Xiao Wang [view email]
[v1]
Wed, 23 Sep 2026 10:35:18 UTC (7,120 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
ProMoE-FL introduces a Prototype-conditioned Mixture-of-Experts framework for multimodal federated learning, effectively addressing missing modalities. It outperforms existing methods on four chest X-ray datasets, demonstrating superior feature synthesis capabilities in both homogeneous and heterogeneous settings.